Regression

A mathematical technique used to explain and/or predict. The general form is Y = a + bX + u, where Y is the variable that we are trying to predict; X is the variable that we are using to predict Y, a is the intercept; b is the slope, and u is the regression residual. The a and b are chosen in a way to minimize the squared sum of the residuals. The ability to fit or explain is measured by the R-square.

Regression Analysis

In statistics, the analysis of variables that are dependent on other variables. Regression analysis often uses regression equations, which show the value of a dependent variable as a function of an independent variable. For example, a regression could take the form:

y = a + bx

where y is the dependent variable and x is the independent variable. In this case, the slope is equal to b and a is the intercept. When plotted on a graph, y is determined by the value of x. Regression equations are charted as a line and are important in calculating economic data and stock prices.

regression

(1) A statistical technique for creating a mathematical equation to explain the relationship between known variables so that the model can be used to predict other variables when one has insufficient data. Multiple regression analysis is the basis of computerized automatic valuation models (AVM) employed instead of appraisals by many mortgage lenders. (2) An appraisal principle that if properties of relatively unequal value are located near each other, the one with the lower value will depress the value of the other. (3) A withdrawal of the sea from the land due to an uplift of the land or a drop in sea level.

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References in periodicals archive

Modified Regression Rate Formula for Uppermost Stage.

Modified Regression Rate Formula of PMMA Combustion by a Single Plane Impinging Jet

681 samples are taken to set up a regression model between sucrose content (Suc) and polarization (Pol).

Analysis of Sugarcane Juice Quality Indexes

A simple answer to this question is the different assumptions between the univariate and multiple regression models.

Two paradoxes in linear regression analysis

Based on disc volume of the T2-weighted MR images; four patients (10%) did not show any regression, six patients (15%) had a partial regression, and 30 patients (75%) had a complete resolution.

Spontaneous regression of extruded lumbar disc herniation: Correlation with clinical outcome

Using factor scores analysis in multiple regression analysis make available an acceptable opportunity of both obtaining uncorrelated-meaningful latent-new independent variables, derived as factor scores from original leashed independent variables, and eliminating multicollinearity problem for ensuring reliability of regression coefficients, as also described previously by many researchers (Eyduran et al., 2009, 2010; Keskin et al., 2007a, b).

Use of Factor Scores in Multiple Regression for Predicting Live Weight in Indigenous Savak Akkaraman Lambs

r = Multiple regression co-efficient###p<0.05= Significant###NH3 = Total ammonia (mg L-1)###Ni = Nitrates (mg L-1)

RELATIONSHIPS AMONG INCREASE IN BIOMASS, PLANKTONIC PRODUCTIVITY AND PHYSICO-CHEMISTRY OF PONDS STOCKED WITH Zn+Pb+Mn MIXTURE STRESSED FISH

Three possible mechanisms may explain the spontaneous regression of these lesions: (1) skeletal maturation is followed by the cease of osteochondroma and lesion was fused into growing metaphysis;[8] (2) bony repair and remodeling process following a fracture was associated with an interruption of blood supply; a fracture of the stalk of a pedunculated osteochondroma may have accelerated resolution of the lesion;[9] and (3) resorption of an osteochondroma occurred due to the presence of an accompanying pseudoaneurysm.[1]

Spontaneous regression of osteochondroma of the distal femur: A Pediatric case report and literature review

His wife suggested that he undergo regression. It turned out that in a past-life encounter, the other guy killed him by hacking his neck with a bolo.

What happens during a past-life regression?

If this pattern is not satisfactory, you can use the multi-linear regression that has the form:

TAKING DECISION BASED ON THE REGRESSION MODEL USING EXCEL 2016

"First, each appraisal would require 37 sales and then you still have the problem of whether the data set (sample size) is large enough." To estimate the coefficients of a multivariate model using the ordinary least squares (OLS), all that is required is that the number of observations should exceed the number of explanatory variables by one for the sample regression model to be estimated successfully.

"How to Interpret Regression Coefficients and Calculate Adjustments for Differences in Property Productivity Features"

Spontaneous regression of structurally incomplete response to initial papillary thyroid cancer treatment is exceedingly rare.

Spontaneous Regression of Metastatic Papillary Thyroid Cancer in a Lymph Node